Gaussian Process Regression Based Silver Price Forecasts

Abstract

A significant number of market participants have placed a high level of importance on price estimates for the primary metal commodities for a considerable amount of time. To tackle the problem, we investigate the daily reported price of silver in our study. The sample that is being analyzed spans a period of 13 years, starting on April 20, 2011, and ending on April 19, 2024. The price series which is being investigated has important implications for the commercial world. Specifically, when it comes to this unique circumstance, Gaussian process regression models are developed utilizing cross-validation strategies and Bayesian optimization procedures. The forecasting of prices is therefore accomplished via the use of the methods that are developed as a result of the situation. For the out-of-sample evaluation period that extends from October 5, 2021 to April 19, 2024, our empirical forecasting approach yields price estimates deemed reasonably accurate. The relative root mean square error reached for the silver price is 0.2257%, with the corresponding root mean square error of 0.0515, mean absolute error of 0.0389, and correlation coefficient of 99.967%. Due to the availability of models that forecast prices, investors and governments are supplied with the information they need to make educated judgments on the silver market by providing them with the knowledge they require. The framework of the Gaussian process regression with Bayesian optimizations demonstrates its good potential for modeling and forecasting sophisticated commodity price series for market participants.